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ALAHAD is a thread-safe library of Fortran 95 packages for solving nonlinear optimization problems. At present, the areas covered by the library are unconstrained and bound-constrained optimization, quadratic programming, nonlinear programming, systems of nonlinear equations and inequalities, and nonlinear least squares problems. GALAHAD contains the following major packages:

ANCELOT B

is the latest release of LANCELOT, and is designed to solve large-scale optimization problems involving the minimization of a nonlinear objective, subject (perhaps) to linear or nonlinear equality and box constraints. All functions involved are assumed to be group partially separable, and the minimization is based on a Sequential Augmented Lagrangian algorithm. The new release is coded in Fortran 95, allows for a non-monotone descent strategy, Moré and Toraldo-type projections, optional use of Lin and Moré's ICFS preconditioner, structured trust regions, and more.

ANCELOT_SIMPLE

is a simple-minded interface to LANCELOT for small, dense problems.

ILTRANE

is a package for finding a feasible point for a set of linear and/or nonlinear equations and inequalities using a multi-dimensional filter trust-region approach. In the event that the system is inconsistent, a local measure of infeasibility is minimized. Core linear algebraic requirements are handled by an adaptive preconditioned CG/Lanczos iteration.

PB

solves quadratic programs using a primal-dual interior-point method globalized by means of a trust region. The method works in two phases, the first finding a feasible point for the set of constraints, while the second maintaining feasibility while iterating towards optimality. Once again, core linear algebraic requirements are handled by an adaptive preconditioned CG/Lanczos iteration.

PA

is an active-set quadratic programming solver which can also treat l_1 quadratic programs. At each iteration, a step is computed along a direction which solves an equality-constrained quadratic program whose constraints are defined by a working subset of the currently active constraints. Although, in general, QPB is to be preferred, QPA is most useful when warm-starting a perturbation of a previously-solved problem.

PC

is a crossover quadratic programming solver which applies successively QPB and QPA. The former is used to obtain a good estimate of the solution at low cost, while the latter refines the solution. Of particular note, the optimal active set given by QPC is more reliable than that from QPB.

RESOLVE

is a quadratic program preprocessor. It aims to apply inexpensive transformations to a given quadratic program, so as to simplify it before passing it to one of the other GALAHAD solvers. Once the solver has completed its task, a post-processing stage may be used to recover the original problem and its solution.

SQP

uses a primal-dual interior-point method to solve a linear or separable convex quadratic program. Alternatively, it may also be used to compute the analytic center of the feasible set, if it exists.

CP

uses a primal-dual interior-point method to find a well-centered interior point for a region defined by a finite number of linear equality and inequality constraints. If the region is feasible but has no interior, inequality constraints which must always be active are identified.

LTR

solves the problem of minimizing a quadratic subject to an ellipsoidal trust-region constraint, using a Krylov method. The algorithm does not require any factorization of the Hessian and may also be used to minimize the quadratic over the boundary of the trust region.

LRT

solves the problem of minimizing a quadratic subject plus a regularisation term penalising a weighted two-norm of the variables, using a Krylov method. Again, the algorithm does not require any factorization of the Hessian.

STR

solves the problem of minimizing the two-norn of the deviation Ax-b subject to an ellipsoidal trust-region constraint, using a Krylov method. The algorithm does not require any factorization of A.

SRT

solves the problem of minimizing the square of the two-norn of the deviation Ax-b plus a regularisation term penalising the two-norm of the variables, using a Krylov method. Again, the algorithm does not require any factorization of A.

2RT

solves the problem of minimizing the two-norn of the deviation Ax-b plus a regularisation term penalising the two-norm of the variables, using a Krylov method. Once again, the algorithm does not require any factorization of A.

QP

solves quadratic programming problems for which all the constraints are equations.

DC

determines whether a system of linear equations is of full rank, and identifies a maximal subset which may be removed without changing the rank

BLS

provides a variety of preconditioners which may be applied when solving saddle point (alternatively augmented, KKT) systems of linear equations.